Data Mining - Concepts and Techniques.

The extraction of pavement cracks is always a hard task in image processing. In airport and road construction, cracking is the main factor for pavement damage, which can decrease the quality of pavement and affect transportation seriously. Cracks also exist in other artificial or natural objects, such as buildings, bridges, tunnels, etc. Among all the object images, pavement crack images are the most complex, so the image processing and analysis for them is harder than other crack images. From the early image acquisition based on photography technology to the current 3D laser scanning technology, the pavement crack image acquisition technology is becoming more convenient and efficient, but there are still challenges in the automatic processing and recognition of cracks in images. From the early global thresholding to deep learning algorithms, the research for crack extraction has been developed for about 40 years. There are many methods and algorithms that are satisfactory in pavement crack applications, but there is no standard until today. Therefore, in order to know the developing history and the advanced research, we have collected a number of literature in this research topic for summarizing the research artwork status, and giving a review of the pavement crack image acquisition methods and 2D crack extraction algorithms. Also, for image acquisition methods and pavement crack image segmentation, more detailed comparison and discussions are made.

Pavement crack image acquisition methods and crack extraction algorithms: A review

Optimal Wiener and homomorphic filtration

An integrated data analytics process to optimize data governance of non-profit organization

In order to obtain more precise information regarding rock fractures, images of a rock sample using two different light sources were acquired and fused. For image acquisition, an epoxy resin liquid was first injected into a fracture zone in situ, and when the epoxy resin was dry, the rock core sample, including the epoxy resin, was removed from the rock base. The rock core sample was then cut into multiple slices and images of the slices using ultraviolet (UV) and visible lighting were acquired. In order to fuse two slice images, an algorithm based on the redundant lifting, non-separable wavelet transform was studied and utilised. Fusion includes three primary steps for each pair of slice images: (1) applying the redundant lifting, non-separable wavelet transform to each image, and then approximating the two images separately; (2) fusing the approximated images corresponding to the decomposition level using certain rules and fusion operators for obtaining fusion coefficients; and (3) applying the fused wavelet coefficients to the redundant lifting non-separable wavelet transform. The results show that combining the proposed method of image acquisition and the image fusion algorithm is not only effective at obtaining a large volume of detailed rock fracture information, it is also an economical and easy to use method. By applying the new method, rock fractures can be easily detected, and many different parameters in different rock engineering applications can be measured and analyzed.

Rock fracture image acquisition using two kinds of lighting and fusion on a wavelet transform

With the increase demand of better image quality lot of image processing algorithms are designed. In this paper we described a method to remove the motion blur present in the image taken form any cameras. Motion blur is the result of the relative motion between the camera and the scene during image exposure time, this includes both camera and scene objects motion.. The aim of image restoration is to reconstruct or estimate an uncorrupted image by using the degraded version of the same image. [2] The most closely related research can be found in Blind deconvolution, and Wiener filter methods, They adopts regularized iteration to restore the degraded image. This work proposes the implementation of Wiener filter with Image fusion to reduce the computational complexity with better acceptable restoration results of image restoration method.. The performance of the every stage is tabulated for the parameters like SNR and RMSE of the restored images.

/pdf/method-for-image-restoration-using-wavelet-based-image-3yqhg1f3b3.pdf

Method for Image Restoration using Wavelet based Image Fusion

Mining association rules at multiple levels helps in finding more specific and relevant knowledge. While computing the number of frequency of an item we need to scan the given database many times. So we used counting inference approach for finding frequent itemsets at each concept levels which reduce the number of scan. In this paper, we purpose a new algorithm LWFT which follow the topdown progressive deepening method and it is based on existing algorithms for finding multiple level association rules. This algorithm is efficient for finding frequent itemsets from large databases.

/pdf/mining-frequent-patterns-with-counting-inference-at-multiple-4hj2ht5dma.pdf

Mining Frequent Patterns with Counting Inference at Multiple Levels

The premise of this paper is to use an efficient encoding scheme which will be used to encode high level concept hierarchy of a transactional table. This table will work as the base to generate multiple level association rules. These rules discovers the hidden knowledge align at higher level of abstraction. Therefore the numeric encoding of the concept hierarchy improves the time complexity and space complexity of task relevant data.

/pdf/representation-of-concept-hierarchy-using-an-efficient-3n5db5hpzo.pdf

Representation of Concept Hierarchy using an Efficient Encoding Scheme

Discovering the association rules among the large databases is the most important feature of data mining. Many algorithms had been introduced by various researchers for finding association rules. Among these algorithms, the FP-growth method is the most proficient. It mines the frequent item set without candidate set generation. The setbacks of FP growth are, it requires two scans of overall database and it uses large number of conditional FP tree to generate frequent itemsets. To overcome these limitations a new approach has been proposed by the name TransTrie, it will use the reduced sorted transposed database. After this it will scan the database and generate a TRIE, in the same step it will also compute the occurrences of each item. Then, using Depth first traversal it will identify the maximal itemsets, from which all frequent itemsets are derived using apriori property.  It also counts the support of frequent itemsets which are used to find the valuable association rules.

https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/download/863/559

An Improved Association Rule Mining Technique Using Transposed Database

Everybody find ease and comfort by using their natural languages to communicate. There is large quantity of natural language material and for this reason there is the need of computer to involve in this process. Beside this people need to communicate with machines and people find natural languages natural. Although there are people who thinks, that only people can effectively use natural languages and thus it is inappropriate to bring computers into this arena, there is already evidence, that programs that manipulate language in various ways can be useful. In this paper, we are extracting information for user based natural language query on the health domain. Information retrieval through such Q/A systems is important sources to help physicians make decisions in patient treatment and as a result, to enhance the quality of patient care by retrieving a vast amount of information in response to a specific user query.

/pdf/computation-of-user-based-query-using-natural-language-51u9ffyxax.pdf

Ruchika Yadav

Papers

Method for Image Restoration using Wavelet based Image Fusion

Mining Frequent Patterns with Counting Inference at Multiple Levels

Representation of Concept Hierarchy using an Efficient Encoding Scheme

An Improved Association Rule Mining Technique Using Transposed Database

Computation of User- based Query using Natural Language Processing